@inproceedings{wang-etal-2020-correcting,
title = "Correcting the Misuse: A Method for the {C}hinese Idiom Cloze Test",
author = "Wang, Xinyu and
Zhao, Hongsheng and
Yang, Tan and
Wang, Hongbo",
editor = "Agirre, Eneko and
Apidianaki, Marianna and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.deelio-1.1/",
doi = "10.18653/v1/2020.deelio-1.1",
pages = "1--10",
abstract = "The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context. Chinese idiom is a type of Chinese idiomatic expression. The common misuse of Chinese idioms leads to error in corpus and causes error in the learned semantic representation of Chinese idioms. In this paper, we introduce the definition written by Chinese experts to correct the misuse. We propose a model for the Chinese idiom cloze test integrating various information effectively. We propose an attention mechanism called Attribute Attention to balance the weight of different attributes among different descriptions of the Chinese idiom. Besides the given candidates of every blank, we also try to choose the answer from all Chinese idioms that appear in the dataset as the extra loss due to the uniqueness and specificity of Chinese idioms. In experiments, our model outperforms the state-of-the-art model."
}
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<abstract>The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context. Chinese idiom is a type of Chinese idiomatic expression. The common misuse of Chinese idioms leads to error in corpus and causes error in the learned semantic representation of Chinese idioms. In this paper, we introduce the definition written by Chinese experts to correct the misuse. We propose a model for the Chinese idiom cloze test integrating various information effectively. We propose an attention mechanism called Attribute Attention to balance the weight of different attributes among different descriptions of the Chinese idiom. Besides the given candidates of every blank, we also try to choose the answer from all Chinese idioms that appear in the dataset as the extra loss due to the uniqueness and specificity of Chinese idioms. In experiments, our model outperforms the state-of-the-art model.</abstract>
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<identifier type="doi">10.18653/v1/2020.deelio-1.1</identifier>
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%0 Conference Proceedings
%T Correcting the Misuse: A Method for the Chinese Idiom Cloze Test
%A Wang, Xinyu
%A Zhao, Hongsheng
%A Yang, Tan
%A Wang, Hongbo
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-correcting
%X The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context. Chinese idiom is a type of Chinese idiomatic expression. The common misuse of Chinese idioms leads to error in corpus and causes error in the learned semantic representation of Chinese idioms. In this paper, we introduce the definition written by Chinese experts to correct the misuse. We propose a model for the Chinese idiom cloze test integrating various information effectively. We propose an attention mechanism called Attribute Attention to balance the weight of different attributes among different descriptions of the Chinese idiom. Besides the given candidates of every blank, we also try to choose the answer from all Chinese idioms that appear in the dataset as the extra loss due to the uniqueness and specificity of Chinese idioms. In experiments, our model outperforms the state-of-the-art model.
%R 10.18653/v1/2020.deelio-1.1
%U https://aclanthology.org/2020.deelio-1.1/
%U https://doi.org/10.18653/v1/2020.deelio-1.1
%P 1-10
Markdown (Informal)
[Correcting the Misuse: A Method for the Chinese Idiom Cloze Test](https://aclanthology.org/2020.deelio-1.1/) (Wang et al., DeeLIO 2020)
ACL
- Xinyu Wang, Hongsheng Zhao, Tan Yang, and Hongbo Wang. 2020. Correcting the Misuse: A Method for the Chinese Idiom Cloze Test. In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 1–10, Online. Association for Computational Linguistics.